👀 Views: 63 💬 Answers: 1 📅 Created: 2025-06-09

aws lambda boto3 s3 Python

I'm working with an `ResourceLimitExceeded` behavior when my AWS Lambda function processes S3 events triggered by uploads in a specific bucket. The Lambda function is configured with 128 MB of memory and a timeout of 3 seconds, but I'm still hitting resource limits when trying to read and process the uploaded files using Boto3. Here’s the relevant part of my Lambda function: ```python import json import boto3 def lambda_handler(event, context): s3 = boto3.client('s3') for record in event['Records']: bucket_name = record['s3']['bucket']['name'] object_key = record['s3']['object']['key'] try: response = s3.get_object(Bucket=bucket_name, Key=object_key) data = response['Body'].read().decode('utf-8') # Process data here except Exception as e: print(f'behavior processing object {object_key} from bucket {bucket_name}. behavior: {e}') ``` In my testing, I'm trying to upload files around 10 MB, and the function seems to unexpected result consistently with the behavior: `Resource limit exceeded while processing 10 MB file`. I have tried increasing the timeout to 5 seconds, but I still encounter the same behavior. I also verified that the Lambda execution role has the necessary permissions to access S3. Additionally, I’ve checked the CloudWatch logs for memory usage, and it seems to be hovering around 90% at the time of the behavior. I’m unsure whether it’s a memory scenario or if I need to optimize how I’m reading and processing the S3 object data. What steps can I take to resolve this scenario? Should I consider increasing the memory allocation for the Lambda function, or is there a better approach to handle larger S3 objects efficiently? Any pointers in the right direction?